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Meta interview notes, page 210 of 238

FAANG / Big Tech

Most of these Meta write-ups come from Software Engineers (1,194), then Data Scientists (489) and Product Managers (453). The most reported rounds are the technical phone screen (1,190), the product sense round (466), the system design round (255) and the behavioral / leadership round (205).

2377 interview notes · updated Jul 2026

Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE coding round with two back-to-back problems, one algorithmic and one design-heavy. The second question especially felt like it kept growing the longer I talked, which was both interesting and a little exhausting.
  • Given two non-negative integers as decimal strings, return their sum as a string without using any built-in big-integer libraries. Walk through the function signature, edge cases, example inputs and outputs, and time/space complexity.
  • Design a weighted random city picker: given a list of city names and their populations, implement an init function and a pickCity function that returns each city with probability proportional to its population. Target O(n) preprocessing and O(log n) or better per pick. Also discuss how you'd validate randomness, avoid precision issues with large population totals, and support dynamic updates like insertions, deletions, and population changes.

“I've done grade-school addition in code before so the core loop wasn't the problem.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round for a Data Engineer role at Meta. Four main topics, each with follow-up questions that go pretty deep, so having a vague story ready won't cut it.
  • Tell me about a time you had a conflict with someone on your team and how you resolved it.
  • Describe a situation where you had to deliver something under a very tight deadline. What did you do and what was the outcome?
  • Walk me through a significant project you led. What was your role and how did you drive it forward?
  • Give me an example of when you used data to influence a decision that others were skeptical about.

“The follow-ups are where this gets uncomfortable.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview with a product design question framed around Zuckerberg himself asking you to build something. Short on details but the question itself is meaty enough to chew on for a while.
  • If Mark Zuckerberg asked you to design a product specifically for athletes, what would you build?

“The Zuckerberg framing is a weird little twist.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta data scientist interview with a meaty product analytics case that took up basically the whole session. The question was layered enough that I kept second-guessing whether I was going too deep or not deep enough.
  • You notice that account-switching events went up while overall active users went down. Walk through how you'd diagnose this, including hypotheses, the metrics you'd pull, slice analyses, any recent experiment or launch correlation checks, a rough SQL sketch, and what you'd actually do if the culprit turned out to be a logging bug, auth friction, or users gaming account limits.

“This one sprawled fast.”

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Meta·Data Scientist·Onsite - Product Sense / Strategy

Jun 2026
Interviewed for a Data Scientist role at Meta and got a visualization-heavy product analytics question that I was not fully prepared for. The whole thing felt more like a product design exercise than a data science interview, which threw me a bit.
  • You're given a spaghetti chart of regional trends for a single metric and asked to redesign it into an executive dashboard. Walk through your layout choices, which 6-8 metrics you'd include for an early-stage B2B chat app, how you'd make regions comparable, your annotation rules, and your visualization guardrails. How does your redesign surface small-country trends that the original chart buries?

“This was a beast of a question and I kind of underestimated it at first.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta Research Scientist coding round with a 2D array manipulation problem. Nothing flashy, but it had enough moving parts to trip you up if you weren't careful about operation order.
  • Given a 2D array and a list of instructions, execute them in sequence. Operations include swapping two rows, swapping two columns, reversing a row, reversing a column, and rotating the entire matrix 90 degrees (implemented as a transpose followed by reversing each row).

“The individual operations aren't hard to code but keeping them straight under pressure is a different story.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, one question on prefix finding. Pretty algorithmic, they pushed for optimal complexity which is where it got interesting.
  • Given a list of strings, find the shortest unique prefix for each string and return them as a list.

“My first instinct was brute force, comparing each string against all others character by character.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, one question about parsing and evaluating a math expression from a string. Pretty classic but the edge cases will get you if you're not careful.
  • Given a mathematical expression as a string, compute and return its result.

“I went straight to recursion and got tripped up on operator precedence.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, one problem the whole time about storing vectors with duplicates and computing dot products efficiently. Pretty focused session, felt like they cared more about the design reasoning than just getting to a working answer.
  • Design a space-efficient data structure for storing integer vectors that may contain many duplicate or zero values, then implement a dot product function that computes the dot product with another such vector efficiently.

“My first instinct was to just store the raw array and call it a day, but the 'space-efficient' part is doing a lot of work in this prompt.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta DS interview with a meaty experimentation question about network effects and cluster-based experiment design. The kind of question where you think you know the answer until they start asking follow-ups.
  • You're running an A/B test on a new recommendation algorithm and need to limit spillover between users. Walk through how you'd use clustered random sampling to assign test and control groups, what kinds of clusters make sense here, why this approach reduces interference, and what trade-offs it introduces.

“I knew the high-level answer (use geographic markets or friend graph communities as clusters so treatment doesn't bleed across the boundary) but fumbled when pushed on the power implications.”

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